Alphaear Sentiment Agent Skill

Analyze finance text sentiment using FinBERT or LLM. Use when the user needs to determine the sentiment (positive/negative/neutral) and score of financial text markets.

11k tokens

context cost

the whole folder, loaded on every use

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

2747

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/RKiding/Awesome-finance-skills --skill alphaear-sentiment

The instruction itself

9 sections, as written by the author

AlphaEar Sentiment Skill

Overview

This skill provides sentiment analysis capabilities tailored for financial texts, supporting both FinBERT (local model) and LLM-based analysis modes.

Capabilities

1. Analyze Sentiment (FinBERT / Local)

Use scripts/sentiment_tools.py for high-speed, local sentiment analysis using FinBERT.

Key Methods:

analyze_sentiment(text): Get sentiment score and label using localized FinBERT model.
Returns: {'score': float, 'label': str, 'reason': str}.
Score Range: -1.0 (Negative) to 1.0 (Positive).
batch_update_news_sentiment(source, limit): Batch process unanalyzed news in the database (FinBERT only).

2. Analyze Sentiment (LLM / Agentic)

For higher accuracy or reasoning capabilities, YOU (the Agent) should perform the analysis using the Prompt below, calling the LLM directly, and then update the database if necessary.

Sentiment Analysis Prompt

Use this prompt to analyze financial texts if the local tool is insufficient or if reasoning is required.

请分析以下金融/新闻文本的情绪极性。
返回严格的 JSON 格式:
{"score": <float: -1.0到1.0>, "label": "<positive/negative/neutral>", "reason": "<简短理由>"}

文本: {text}

Scoring Guide:

Positive (0.1 to 1.0): Optimistic news, profit growth, policy support, etc.
Negative (-1.0 to -0.1): Losses, sanctions, price drops, pessimism.
Neutral (-0.1 to 0.1): Factual reporting, sideways movement, ambiguous impact.

Helper Methods

update_single_news_sentiment(id, score, reason): Use this to save your manual analysis to the database.

Dependencies

torch (for FinBERT)
transformers (for FinBERT)
sqlite3 (built-in)

Ensure DatabaseManager is initialized correctly.

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How to use it

Copy the folder

Take rkiding/alphaear-sentiment from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.